Literature DB >> 22107590

All scale-free networks are sparse.

Charo I Del Genio1, Thilo Gross, Kevin E Bassler.   

Abstract

We study the realizability of scale-free networks with a given degree sequence, showing that the fraction of realizable sequences undergoes two first-order transitions at the values 0 and 2 of the power-law exponent. We substantiate this finding by analytical reasoning and by a numerical method, proposed here, based on extreme value arguments, which can be applied to any given degree distribution. Our results reveal a fundamental reason why large scale-free networks without constraints on minimum and maximum degree must be sparse.
© 2011 American Physical Society

Year:  2011        PMID: 22107590     DOI: 10.1103/PhysRevLett.107.178701

Source DB:  PubMed          Journal:  Phys Rev Lett        ISSN: 0031-9007            Impact factor:   9.161


  21 in total

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2.  Sparse graphs using exchangeable random measures.

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3.  Motif Participation by Genes in E. coli Transcriptional Networks.

Authors:  Michael Mayo; Ahmed F Abdelzaher; Edward J Perkins; Preetam Ghosh
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4.  Two universal physical principles shape the power-law statistics of real-world networks.

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5.  From sparse to dense and from assortative to disassortative in online social networks.

Authors:  Menghui Li; Shuguang Guan; Chensheng Wu; Xiaofeng Gong; Kun Li; Jinshan Wu; Zengru Di; Choy-Heng Lai
Journal:  Sci Rep       Date:  2014-05-06       Impact factor: 4.379

6.  Low-dimensional behavior of Kuramoto model with inertia in complex networks.

Authors:  Peng Ji; Thomas K D M Peron; Francisco A Rodrigues; Jürgen Kurths
Journal:  Sci Rep       Date:  2014-05-02       Impact factor: 4.379

7.  Degree correlations in directed scale-free networks.

Authors:  Oliver Williams; Charo I Del Genio
Journal:  PLoS One       Date:  2014-10-13       Impact factor: 3.240

8.  Multiplex congruence network of natural numbers.

Authors:  Xiao-Yong Yan; Wen-Xu Wang; Guan-Rong Chen; Ding-Hua Shi
Journal:  Sci Rep       Date:  2016-03-31       Impact factor: 4.379

9.  Network Topologies Decoding Cervical Cancer.

Authors:  Sarika Jalan; Krishna Kanhaiya; Aparna Rai; Obul Reddy Bandapalli; Alok Yadav
Journal:  PLoS One       Date:  2015-08-26       Impact factor: 3.240

Review 10.  Transcriptional Network Growing Models Using Motif-Based Preferential Attachment.

Authors:  Ahmed F Abdelzaher; Ahmad F Al-Musawi; Preetam Ghosh; Michael L Mayo; Edward J Perkins
Journal:  Front Bioeng Biotechnol       Date:  2015-10-12
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